Distinctive features of speech are the fundamental characteristics that make each phoneme in all the languages of the world unique, and are described in Jakobson, R., C. G. M. Fant, and M. Halle, PRELIMINARIES TO SPEECH ANALYSIS: THE DISTINCTIVE FEATURES AND THEIR CORRELATES (MIT Press, Cambridge, Mass.; 1961) (hereinafter “Jakobson et al.”), the disclosure of which is hereby incorporated by reference herein in its entirety. They function to discriminate each phoneme from all others and as such are traditionally identified by the binary extremes of each feature's range. Jakobson et al. defined twelve features that fully discriminate the world's phonemes: 1) vocalic/non-vocalic, 2) consonantal/non-consonantal, 3) compact/diffuse, 4) grave/acute, 5) flat/plain, 6) nasal/oral, 7) tense/lax, 8) continuous/interrupted, 9) strident/mellow, 10) checked/unchecked, 11) voiced/unvoiced, and 12) sharp/plain.
Distinctive features are phonological, developed primarily to express in a simple manner the rules of a language for combining phonetic segments into meaningful words, and are described in Mannell, R., Phonetics & Phonology topics: Distinctive Features, http://clas.mq.edu.au/speech/phonetics/phonology/featurcs/index.html (accessed Feb. 18, 2009) (hereinafter “Mannell”), the disclosure of which is hereby incorporated by reference herein in its entirety. However, distinctive features are manifest in spoken language through acoustic correlates. For example, “compact” denotes a clustering of formants, while “diffuse” denotes a wide range of formant frequencies of a phoneme. All twelve distinctive features may be expressed in terms of acoustic correlates, as described in Jakobson et al., which are measurable from speech waveforms. Jakobson et al. suggest measures for acoustic correlates; however, such measures are neither unique nor optimal in any sense, and many measures exist which may be used as acoustic correlates of distinctive features.
Distinctive features, through acoustic correlates, are naturally related to speech intelligibility, because a change in distinctive feature (e.g., tense to lax) results in a change in phoneme (e.g., /p/ to /b/) which produces different words when used in the same context (e.g., “pat” and “bat” are distinct English words). Highly intelligible speech contains phonemes that are easily recognized (quantified variously by listener cognitive load or noise robustness) and exhibits acoustic correlates that are highly separable. Conversely, speech of low intelligibility contains phonemes that are easily confused with others and exhibits acoustic correlates that are not highly separable. Therefore, the separability of acoustic correlates of distinctive features is a measure of the intelligibility of speech. Separation of acoustic correlates of distinctive features may be measured in several ways. Distinctive features naturally separate into binary classes, so classification methods may be used to map acoustic correlates to speech intelligibility. Binary classes, however, do not produce sufficient differentiation between the distinctive features. What is needed, then, is a method that measure speech intelligibility with higher resolution than the known binary classes.